Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

Abstract

Ground-based experimental evaluations of emerging guidance, navigation, and control (GNC) approaches may be used to raise their technological readiness level and determine their performance and limitations on flight-equivalent hardware (i.e., sensors, actuators, and computational systems) [2].An experimental campaign to evaluate the performance of the model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD) guidance methods has been performed at the Naval Postgraduate School POSEI-DYN 1 air-bearing test bed [4]. The focus of this research is limited in scope to the guidance and control of the simulated spacecraft. The navigation problem is solved by the POSEIDYN test bed motion capture system, which, augmented by onboard sensors, is used to provide accurate navigation data. The test vehicles operating in the POSEI-DYN test bed float on top of a 4-by-4 m granite table and exhibit a drag-free and weightless motion on a plane [4]. These test vehicles are referred to as floating spacecraft simulators, or simply as FSS.A spacecraft docking problem is selected for the experimental evaluation of these two different control approaches. A keep-out zone, an entry cone, and a maximum force constraint are added to the docking scenario to evaluate the constraint handling abilities of the two different controllers. A linear-quadratic MPC (LQ-MPC) algorithm with a quadratic programming (QP) solver and an IDVD algorithm with a nonlinear programming (NLP) solver have been chosen for this comparative study. These two controllers have been implemented and, when executed in real-time on board the FSS, they are successful in 1 POSEIDYN stands for Proximity Operation of Spacecraft: Experimental hardware-In-the-loop DYNamic simulator Abstract An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a groundbased air-bearing test bed, using spacecraft simulators that float over a granite table.Keywords Rendezvous and proximity operations · Model predictive control · Inverse dynamics · Hardware-in-theloop

Autonomous Capture of a Resident Space Object by a Spacecraft with a Robotic Manipulator: Analysis, Simulation and Experiments

“…The chaser is equipped with a four-link serial manipulator, which also floats on the granite monolith. This set-up provides a planar, yet dynamically equivalent, environment in which to experimentally evaluate spacecraft proximity operations maneuvers (see examples of recent efforts 15,[23][24][25] ). Figure 2 shows an overview of the test bed.…”

Adaptive Saturated Neural Network Tracking Control of Spacecraft: Theory and Experimentation

“…where κ 2 > 0. By substituting equation (20) into equation (16), and equation (24) and (25) into equation (22), a 6DOF closedloop system is derived as follows…”

“…where θ i , i = 1, 2, 3, is an arbitrary positive constant, and β = sup ðkBk 2 Þ, the proposed controller (25) and adaptation laws (26) ensure that the 6DOF tracking objective is achieved, i.e., x 1 and x 2 converge to small sets around zero, resectively.…”

“…To realize the theoretical results in practical applications, the air-bearing ground test facilities have been developed [18,19] and some controllers have been validated by hardware-in-the-loop experiments [20][21][22][23][24][25][26]. For spacecraft operations subject to parametric uncertainties, adaptive controllers are designed and the related experimental validations are conducted on the ground test facilities [20,21].…”

“…For spacecraft operations, PID [22] and LQR [23] controllers are designed and experimentally tested, respectively. In addition, several controllers [24][25][26] are developed and validated on the testbed facility. Despite the fact that experimental results can be found in [20][21][22][23][24][25][26], the NN-based controllers are rarely validated by hardware-in-the-loop experiments in previous results.…”